OpenRaiser/Intern-Atlas huggingface dataset:
Leader Follower Formation Control of Ground Vehicles Using Camshift Based Guidance
Venue: arXiv cs.CV 2015
Year: 2015

Autonomous ground vehicles have been designed for the purpose of that relies on ranging and bearing information received from forward looking camera on the Formation control . A visual guidance control algorithm is designed where real time image processing is used to provide feedback signals. The vision subsystem and control subsystem work in parallel to accomplish formation control. A proportional navigation and line of sight guidance laws are used to estimate the range and bearing information from the leader vehicle using the vision subsystem. The algorithms for vision detection and localization used here are similar to approaches for many computer vision tasks such as face tracking and detection that are based color-and texture based features, and non-parametric Continuously Adaptive Mean-shift algorithms to keep track of the leader. This is being proposed for the first time in the leader follower framework. The algorithms are simple but effective for real time and provide an alternate approach to traditional based approaches like the Viola Jones algorithm. Further to stabilize the follower to the leader trajectory, the sliding mode controller is used to dynamically track the leader. The performance of the results is demonstrated in simulation and in practical experiments.
CSMA/CA Bottleneck Remediation in Saturation Mode with New Backoff Strategy
Venue: arXiv cs.NI 2015
Year: 2015

Many modern wireless networks integrate carrier sense mul-tiple access/collision avoidance (CSMA/CA) with exponential backoff as medium access control (MAC) technique. In order to decrease the MAC overhead and the collision probability, we propose in this paper a new backoff strategy leading to better saturation throughput and access de-lay performance comparing to the classical protocol. We investigate the CSMA/CA with RTS/CTS technique, and we show that our strategy reaches better saturation throughput and access delay especially in dense networks. This proposed strategy distributes users over all the backoff stages to solve the bottleneck problem present in the first backoff stage. Finally, we analyze our strategy and we compare it to the classical one modeled by Markov chain. Analytical and simulation results show the improvment in term of saturation throughput. Cumulative density func-tion (CDF) of the access delay illustrates the important gain obtained by the proposed strategy.
Analysis and Clustering of Workload in Google Cluster Trace based on Resource Usage
Venue: arXiv cs.DC 2015
Year: 2015

Cloud computing has gained interest amongst commercial organizations, research communities, developers and other individuals during the past few years.In order to move ahead with research in field of data management and processing of such data, we need benchmark datasets and freely available data which are publicly accessible. Google in May 2011 released a trace of a cluster of 11k machines referred as Google Cluster Trace.This trace contains cell information of about 29 days.This paper provides analysis of resource usage and requirements in this trace and is an attempt to give an insight into such kind of production trace similar to the ones in cloud environment.The major contributions of this paper include Statistical Profile of Jobs based on resource usage, clustering of Workload Patterns and Classification of jobs into different types based on k-means clustering.Though there have been earlier works for analysis of this trace, but our analysis provides several new findings such as jobs in a production trace are trimodal and there occurs symmetry in the tasks within a long job type
Evidential-EM Algorithm Applied to Progressively Censored Observations
Venue: arXiv cs.AI 2015
Year: 2015

Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incom-plete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior infor-mation with the current imprecise knowledge conveyed by the observed data.
ARQ for Physical-layer Network Coding
Venue: arXiv cs.NI 2015
Year: 2015

This paper investigates ARQ (Automatic Repeat request) designs for PNC (Physical-layer Network Coding) systems. We have previously found that, besides TWRC (Two-Way Relay Channel) operated on the principle of PNC, there are many other PNC building blocks--building blocks are simple small network structures that can be used to construct a large network. In some of these PNC building blocks, receivers can obtain side information through overhearing. Although such overheard information is not the target information that the receivers desire, the receivers can exploit the overheard information together with a network-coded packet received to obtain a desired native packet. This leads to throughput gain. Our previous study, however, assumed what is sent always get received. In practice, that is not the case. Error control is needed to ensure reliable communication. This paper focuses on the use of ARQ to ensure reliable PNC communication. The availability of overheard Information and its potential exploitation make the ARQ design of a network-coded system different from that of a non-network-coded system. In this paper, we lay out the fundamental considerations for such ARQ design: 1) We address how to track the stored coded packets and overheard packets to increase the chance of packet extraction, and derive the throughput gain achieved by tracking 2) We investigate two variations of PNC ARQ, coupled and non-coupled ARQs, and prove that non-coupled ARQ is more efficient; 3) We show how to optimize parameters in PNC ARQ--specifically the window size and ACK frequency--to minimize the throughput degradation caused by ACK feedback overhead and wasteful retransmissions due to lost ACK.
The Cost of Problem-Based Learning: An Example in Information Systems Engineering
Venue: arXiv cs.SE 2015
Year: 2015

High-quality education helps in finding a job - but student skills heterogeneity and student reluctance to move towards a professional attitude are important barriers to employability. We re-engineered some of the technical courses of a Masters in software development using a Problem-Based Learning (PBL) approach. Although initial results are encouraging, the cost of using PBL must be taken into account. Two aspects are particularly expensive: (i) set-up of the software development practicum, a mid-sized information system and its environment; (ii) screenwriting of problem-based learning scenarios, including procurement of input artefacts.
Sparse Solutions to Nonnegative Linear Systems and Applications
Venue: arXiv cs.DS 2015
Year: 2015

We give an efficient algorithm for finding sparse approximate solutions to linear systems of equations with nonnegative coefficients. Unlike most known results for sparse recovery, we do not require {\em any} assumption on the matrix other than non-negativity. Our algorithm is combinatorial in nature, inspired by techniques for the set cover problem, as well as the multiplicative weight update method. We then present a natural application to learning mixture models in the PAC framework. For learning a mixture of $k$ axis-aligned Gaussians in $d$ dimensions, we give an algorithm that outputs a mixture of $O(k/ε^3)$ Gaussians that is $ε$-close in statistical distance to the true distribution, without any separation assumptions. The time and sample complexity is roughly $O(kd/ε^3)^{d}$. This is polynomial when $d$ is constant -- precisely the regime in which known methods fail to identify the components efficiently. Given that non-negativity is a natural assumption, we believe that our result may find use in other settings in which we wish to approximately explain data using a small number of a (large) candidate set of components.
On the Complexity of Sorted Neighborhood
Venue: arXiv cs.CC 2015
Year: 2015

Record linkage concerns identifying semantically equivalent records in databases. Blocking methods are employed to avoid the cost of full pairwise similarity comparisons on $n$ records. In a seminal work, Hernandez and Stolfo proposed the Sorted Neighborhood blocking method. Several empirical variants have been proposed in recent years. In this paper, we investigate the complexity of the Sorted Neighborhood procedure on which the variants are built. We show that achieving maximum performance on the Sorted Neighborhood procedure entails solving a sub-problem, which is shown to be NP-complete by reducing from the Travelling Salesman Problem. We also show that the sub-problem can occur in the traditional blocking method. Finally, we draw on recent developments concerning approximate Travelling Salesman solutions to define and analyze three approximation algorithms.
Online Algorithms Modeled After Mousehunt
Venue: arXiv cs.DS 2015
Year: 2015

In this paper we study a variety of novel online algorithm problems inspired by the game Mousehunt. We consider a number of basic models that approximate the game, and we provide solutions to these models using Markov Decision Processes, deterministic online algorithms, and randomized online algorithms. We analyze these solutions' performance by deriving results on their competitive ratios.
Evidential Communities for Complex Networks
Venue: arXiv cs.SI 2015
Year: 2015

Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify overlapping communi-ties in complex networks by a combination of an evidential modularity function, a spectral mapping method and evidential c-means clustering is devised. Experimental results indicate that this detection approach can take advantage of the theory of belief functions, and preforms good both at detecting community structure and determining the appropri-ate number of clusters. Moreover, the credal partition obtained by the proposed method could give us a deeper insight into the graph structure.
The Hebrew Bible as Data: Laboratory - Sharing - Experiences
Venue: arXiv cs.CL 2015
Year: 2015

The systematic study of ancient texts including their production, transmission and interpretation is greatly aided by the digital methods that started taking off in the 1970s. But how is that research in turn transmitted to new generations of researchers? We tell a story of Bible and computer across the decades and then point out the current challenges: (1) finding a stable data representation for changing methods of computation; (2) sharing results in inter- and intra-disciplinary ways, for reproducibility and cross-fertilization. We report recent developments in meeting these challenges. The scene is the text database of the Hebrew Bible, constructed by the Eep Talstra Centre for Bible and Computer (ETCBC), which is still growing in detail and sophistication. We show how a subtle mix of computational ingredients enable scholars to research the transmission and interpretation of the Hebrew Bible in new ways: (1) a standard data format, Linguistic Annotation Framework (LAF); (2) the methods of scientific computing, made accessible by (interactive) Python and its associated ecosystem. Additionally, we show how these efforts have culminated in the construction of a new, publicly accessible search engine SHEBANQ, where the text of the Hebrew Bible and its underlying data can be queried in a simple, yet powerful query language MQL, and where those queries can be saved and shared.
Interests Diffusion in Social Networks
Venue: arXiv cs.CY 2015
Year: 2015

Understanding cultural phenomena on Social Networks (SNs) and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.
Z-score-based modularity for community detection in networks
Venue: arXiv cs.SI 2015
Year: 2015

Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan [Phys. Rev. E 69, 026113 (2004)] is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given division with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function.
Design, Analysis, and Simulation of a Pipe-Welding Robot with Fixed Plinth
Venue: arXiv cs.OH 2015
Year: 2015

Industrial requirements concerning the increased efficiency and high rate of manufacturing result in the development of manufacturer robots, and a vast group of these types of robots is used for welding. This study presented the design, analysis, and simulation of a pipe-welding robot with fixed plinth for a constant circular welding around the pipes. Design of a welding robot capable of keeping the electrode orientation, welding speed, and distance between electrode and pipe surface constant can improve the quality of welding; thus, a five-linked articulated robot was designed for this purpose. Solving of direct and diverse kinematics and dynamics equations of the robot was done by means of Matlab software. The robot was also simulated using a program written in Matlab and the diagrams of angles, velocities, and accelerations of all the arms, and the applied force and torque of each arm required for drive the mechanism were obtained.
Modeling Hybrid Systems in the Concurrent Constraint Paradigm
Venue: arXiv cs.PL 2015
Year: 2015

Hybrid systems, which combine discrete and continuous dynamics, require quality modeling languages to be either described or analyzed. The Concurrent Constraint paradigm (ccp) is an expressive declarative paradigm, characterized by the use of a common constraint store to communicate and synchronize concurrent agents. In this paradigm, the information is stated in the form of constraints, in contrast to the variable/value style typical of imperative languages. Several extensions of ccp have been proposed in order to model reactive systems. One of these extensions is the Timed Concurrent Constraint Language (tccp) that adds to ccp a notion of discrete time and new features to model time-out and preemption actions. The goal of this paper is to explore the expressive power of tccp to describe hybrid systems. We introduce the language Hy-tccp as a conservative extension of tccp, by adding a notion of continuous time and new constructs to describe the continuous dynamics of hybrid systems. In this paper, we present the syntax and the operational semantics of Hy-tccp together with some examples that show the expressive power of our new language.
Reviving the past: the growth of citations to old documents
Venue: arXiv cs.DL 2015
Year: 2015

In this Digest we review a recent study released by the Google Scholar team on the apparently increasing fraction of citations to old articles from studies published in the last 24 years (1990-2013). First, we describe the main findings of their article. Secondly, we conduct an analogue study, using a different data source as well as different measures which throw very similar results, thus confirming the phenomenon. Lastly, we discuss the possible causes of this phenomenon.
Algorithms for two variants of Satisfaction Approval Voting
Venue: arXiv cs.GT 2015
Year: 2015

Multi-winner voting rules based on approval ballots have received increased attention in recent years. In particular Satisfaction Approval Voting (SAV) and its variants have been proposed. In this note, we show that the winning set can be determined in polynomial time for two prominent and natural variants of SAV. We thank Arkadii Slinko for suggesting these problems in a talk at the Workshop on Challenges in Algorithmic Social Choice, Bad Belzig, October 11, 2014.
Building Mini-Categories in Product Networks
Venue: arXiv cs.SI 2015
Year: 2015

We constructed a product network based on the sales data collected and provided by a Fortune 500 speciality retailer. The structure of the network is dominated by small isolated components, dense clique-based communities, and sparse stars and linear chains and pendants. We used the identified structural elements (tiles) to organize products into mini-categories -- compact collections of potentially complementary and substitute items. The mini-categories extend the traditional hierarchy of retail products (group - class - subcategory) and may serve as building blocks towards exploration of consumer projects and long-term customer behavior.
On model misspecification and KL separation for Gaussian graphical models
Venue: arXiv cs.IT 2015
Year: 2015

We establish bounds on the KL divergence between two multivariate Gaussian distributions in terms of the Hamming distance between the edge sets of the corresponding graphical models. We show that the KL divergence is bounded below by a constant when the graphs differ by at least one edge; this is essentially the tightest possible bound, since classes of graphs exist for which the edge discrepancy increases but the KL divergence remains bounded above by a constant. As a natural corollary to our KL lower bound, we also establish a sample size requirement for correct model selection via maximum likelihood estimation. Our results rigorize the notion that it is essential to estimate the edge structure of a Gaussian graphical model accurately in order to approximate the true distribution to close precision.
Stochastic Contracts for Runtime Checking of Component-based Real-time Systems
Venue: arXiv cs.SE 2015
Year: 2015

This paper introduces a new technique for dynamic verification of component-based real-time systems based on statistical inference. Verifying such systems requires checking two types of properties: functional and real-time. For functional properties, a standard approach for ensuring correctness is Design by Contract: annotating programs with executable pre- and postconditions. We extend contracts for specifying real-time properties. In the industry, components are often bought from vendors and meant to be used off-the-shelf which makes it very difficult to determine their execution times and express related properties. We present a solution to this problem by using statistical inference for estimating the properties. The contract framework allows application developers to express contracts like "the execution time of component $X$ lies within $γ$ standard deviations from the mean execution time". Experiments based on industrial case studies show that this framework can be smoothly integrated into existing control applications, thereby increasing their reliability while having an acceptable execution time overhead (less than 10%).
Fitting the Log Skew Normal to the Sum of Independent Lognormals Distribution
Venue: arXiv cs.IT 2015
Year: 2015

Sums of lognormal random variables (RVs) occur in many important problems in wireless communications especially in interferences calculation. Several methods have been proposed to approximate the lognormal sum distribution. Most of them requires lengthy Monte Carlo simulations, or advanced slowly converging numerical integrations for curve fitting and parameters estimation. Recently, it has been shown that the log skew normal distribution can offer a tight approximation to the lognormal sum distributed RVs. We propose a simple and accurate method for fitting the log skew normal distribution to lognormal sum distribution. We use moments and tails slope matching technique to find optimal log skew normal distribution parameters. We compare our method with those in literature in terms of complexity and accuracy. We conclude that our method has same accuracy than other methods but more simple. To further validate our approach, we provide an example for outage probability calculation in lognormal shadowing environment based on log skew normal approximation.
Low Cost Semi-Autonomous Agricultural Robots In Pakistan-Vision Based Navigation Scalable methodology for wheat harvesting
Venue: arXiv cs.RO 2015
Year: 2015

Robots have revolutionized our way of life in recent years.One of the domains that has not yet completely benefited from the robotic automation is the agricultural sector. Agricultural Robotics should complement humans in the arduous tasks during different sub-domains of this sector. Extensive research in Agricultural Robotics has been carried out in Japan, USA, Australia and Germany focusing mainly on the heavy agricultural machinery. Pakistan is an agricultural rich country and its economy and food security are closely tied with agriculture in general and wheat in particular. However, agricultural research in Pakistan is still carried out using the conventional methodologies. This paper is an attempt to trigger the research in this modern domain so that we can benefit from cost effective and resource efficient autonomous agricultural methodologies. This paper focuses on a scalable low cost semi-autonomous technique for wheat harvest which primarily focuses on the farmers with small land holdings. The main focus will be on the vision part of the navigation system deployed by the proposed robot.